code: def test(model, dataloaders, args):
      model.eval()
      accs = {}
      for mode, dataloader in dataloaders.items():
          acc = 0
          for i, batch in enumerate(dataloader):
              num = 0
              batch.to(args.device)
              pred = model(batch)
              for key in pred:
                  p = torch.sigmoid(pred[key]).cpu().detach().numpy()
                  pred_label = np.zeros_like(p, dtype=np.int64)
                  pred_label[np.where(p > 0.5)[0]] = 1
                  pred_label[np.where(p <= 0.5)[0]] = 0
                  acc += np.sum(
                      pred_label == batch.edge_label[key].cpu().numpy()
                  )
                  num += len(pred_label)
          accs[mode] = acc / num
      return accs
  
imports: import argparse
  import copy
  import time
  import numpy
  import networkx
  import sklearn.metrics
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from deepsnap.hetero_gnn import HeteroSAGEConv, HeteroConv, forward_op
label_desc: Use image node_id 5 for calling a global function with example usage: test(model, dataloaders, args) and returns: accs